For the experiments to validate effectiveness of VAT on semantic correspondence, we used the three most widely used datasets in this task, which include SPair-71k [1]}, PF-PASCAL [2]} and PF-WILLOW [3]}. We made minor modifications to our model: we excluded bilinear upsampling in the affinity-aware decoder, which the original output shape is \(128 \times 128\)  but with the modification, now the output shape is \(32 \times 32\)  as two x2 bilinear upsampling is excluded. We used embedded features from conv4\(\_\)  and conv5\(\_\) x, which is different from the training setting for the experiments on few-shot segmentation datasets. Note that we do not utilize segmentation mask for this task. We only use keypoints provided in the datasets for training similarly to [4]}. We used the same data augmentation scheme [4]}, [6]} as Table REF . We used learning rate of 3e-6 for the feature backbone and 3e-4 for VAT. We used batch size of 8. For the evaluation, we followed the standard protocol [7]}, [8]}, [9]}, [10]}, [4]}, [12]}.
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